{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## NVIDIA Nsight Systems\n",
    "[NVIDIA Nsight™ Systems](https://developer.nvidia.com/nsight-systems) is a system-wide performance analysis tool designed to visualize an application’s algorithms, help to identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## NVIDIA Tools Extension (NVTX)\n",
    "\n",
    "The [NVIDIA® Tools Extension Library (NVTX)](https://github.com/NVIDIA/NVTX) is a powerful mechanism that allows users to manually instrument their application. With a C-based and a python-based Application Programming Interface (API) for annotating events, code ranges, and resources in your applications. Applications which integrate NVTX can use NVIDIA Nsight, Tegra System Profiler, and Visual Profiler to capture and visualize these events and ranges. In general, the NVTX can bring valuable insignt into the application while incurring almost no overhead."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "## MONAI Transforms and NVTX\n",
    "[MONAI](https://github.com/Project-MONAI/MONAI) is a high level framework for deep learning in healthcare imaging. One of its core concepts is [Transforms](https://github.com/Project-MONAI/MONAI/tree/dev/monai/transforms), similar to [TorchVision Transfoms](https://pytorch.org/vision/stable/transforms.html). Several of these transfoms are usualy chained together, using a [Compose](https://github.com/Project-MONAI/MONAI/blob/2f1c7a5d1b47c8dd21681dbe1b67213aa3278cd7/monai/transforms/compose.py#L35) class, to create a preprocessing or postprocessing pipeline that performs manipulation of the input data and make it suitable for training a deep learning model or inference. Using this chain of tranformaion does not allow for insertion of NVTX annotation directly in the code and may require modification of the source code. On the other hand, [MONAI NVTX Tranforms]() are a flexible way to insert the annotations in the chain of transforms by simplly adding additional tranforms that cause vitually no-overhead and does not alter the code.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Setup Environments"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Install dependencies"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "!python -c \"import monai\" || pip install -q git+https://github.com/Project-MONAI/MONAI#egg=MONAI[gdown,ignite,torchvision,cucim]\n",
    "!python -c \"import ignite\" || pip install -q pytorch-ignite\n",
    "!python -c \"import torchvision\" || pip install -q torchvision\n",
    "!python -c \"import cucim\" || pip install -q cucim\n",
    "!python -c \"import pandas\" || pip install -q pandas\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "import os\n",
    "\n",
    "import gdown\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Download data\n",
    "\n",
    "The pipeline that we are profiling `camelyon_train_evaluate_nvtx_profiling.py` required [Camelyon-16 Challenge](https://camelyon16.grand-challenge.org/) dataset.  You can download all the images for \"CAMELYON16\" data set from sources listed [here](https://camelyon17.grand-challenge.org/Data/), as well as the coordinations and labels (`dataset_0.json`), from [here](/view?usp=sharing)\n",
    "\n",
    "However, for the demo of this notebook, we are downloading a very small subset of Camelyon dataaset, which uses only one whole slide image `tumor_091.tif` .\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# Download datset.json\n",
    "dataset_url = \"https://drive.google.com/uc?id=1F-lR9tXoFkPkC1yueM-_TyaFk3CO7v0s\"\n",
    "dataset_path = \"dataset_0.json\"\n",
    "gdown.download(dataset_url, dataset_path, quiet=False)\n",
    "\n",
    "# Download images\n",
    "# by default the images expect to be under training/images/\n",
    "image_dir = os.path.join(\"training\", \"images\", \"\")\n",
    "if not os.path.exists(image_dir):\n",
    "    os.makedirs(image_dir)\n",
    "image_url = \"https://drive.google.com/uc?id=1OxAeCMVqH9FGpIWpAXSEJe6cLinEGQtF\"\n",
    "gdown.download(image_url, image_dir, quiet=False)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1F-lR9tXoFkPkC1yueM-_TyaFk3CO7v0s\n",
      "To: /home/bhashemian/workspace/tutorials/pathology/tumor_detection/ignite/dataset_0.json\n",
      "100%|██████████| 1.10M/1.10M [00:00<00:00, 14.2MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1OxAeCMVqH9FGpIWpAXSEJe6cLinEGQtF\n",
      "To: /home/bhashemian/workspace/tutorials/pathology/tumor_detection/ignite/training/images/tumor_091.tif\n",
      "546MB [00:05, 106MB/s]\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'training/images/tumor_091.tif'"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Profiling Digital Pathology Tumor Detection Pipeline"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "!nsys profile \\\n",
    "    --trace nvtx,osrt,cudnn,cuda, \\\n",
    "    --delay 15 \\\n",
    "    --duration 45 \\\n",
    "    --show-output true \\\n",
    "    --force-overwrite true \\\n",
    "    --output profile_report \\\n",
    "    python camelyon_train_evaluate_nvtx_profiling.py \\\n",
    "        --cpu 0 \\\n",
    "        --dataset ./dataset_0.json \\\n",
    "        --root ./"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Warning: LBR backtrace method is not supported on this platform. DWARF backtrace method will be used.\n",
      "{'dataset_json': './dataset_0.json', 'data_root': './', 'logdir': './logs/', 'region_size': 768, 'grid_shape': 3, 'patch_size': 224, 'batch_size': 64, 'n_epochs': 10, 'lr': 0.001, 'use_openslide': False, 'amp': True, 'novograd': True, 'pretrain': True, 'num_workers': 0, 'gpu': '0'}\n",
      "Logs and model are saved at './logs/210824-141440_resnet18_ps224_bs64_ep10_lr0.001'.\n",
      "CUDA is being used with GPU ID(s): 0\n",
      "[Plugin: cucim.kit.cuslide] Loading...\n",
      "[Plugin: cucim.kit.cuslide] Loading the dynamic library from: /opt/conda/lib/python3.8/site-packages/cucim/clara/cucim.kit.cuslide@0.19.0.so\n",
      "[Plugin: cucim.kit.cuslide] loaded successfully. Version: 0\n",
      "Initializing plugin: cucim.kit.cuslide (interfaces: [cucim::io::IImageFormat v0.1]) (impl: cucim.kit.cuslide)\n",
      "image: \n",
      "    shape torch.Size([576, 3, 224, 224])\n",
      "    type:  <class 'torch.Tensor'>\n",
      "    dtype:  torch.float32\n",
      "labels: \n",
      "    shape torch.Size([576, 1, 1, 1])\n",
      "    type:  <class 'torch.Tensor'>\n",
      "    dtype:  torch.float32\n",
      "batch size: 64\n",
      "train number of batches: 47\n",
      "valid number of batches: 0\n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Engine run resuming from iteration 0, epoch 0 until 10 epochs\n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 1/47 -- train_loss: 0.6424 \n",
      "Collecting data...\n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 2/47 -- train_loss: 0.7074 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 3/47 -- train_loss: 0.7098 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 4/47 -- train_loss: 0.6638 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 5/47 -- train_loss: 0.6902 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 6/47 -- train_loss: 0.6625 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 7/47 -- train_loss: 0.6668 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 8/47 -- train_loss: 0.6652 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 9/47 -- train_loss: 0.6395 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 10/47 -- train_loss: 0.6916 \n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 11/47 -- train_loss: 0.6180 \n",
      "Processing events...\n",
      "INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/10, Iter: 12/47 -- train_loss: 0.6304 \n",
      "Saving temporary \"/tmp/nsys-report-2cde-5e73-51c0-a9e4.qdstrm\" file to disk...\n",
      "\n",
      "\n",
      "Sent signal 15 (Terminated) to target application's process group ID = 740371.\n",
      "Use the kill option to modify this behavior.\n",
      "Creating final output files...\n",
      "Processing [0%                                                                ]\n",
      "The target application terminated with signal 15 (SIGTERM)\n",
      "Processing [==============================================================100%]\n",
      "Saved report file to \"/tmp/nsys-report-2cde-5e73-51c0-a9e4.qdrep\"\n",
      "Report file moved to \"/home/bhashemian/workspace/tutorials/pathology/tumor_detection/ignite/profile_report.qdrep\"\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "!nsys stats \\\n",
    "    --report nvtxppsum,nvtxppsum,nvtxpptrace \\\n",
    "    --format table,csv \\\n",
    "    --output -,. \\\n",
    "    --force-overwrite true \\\n",
    "    profile_report.qdrep"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Generating SQLite file profile_report.sqlite from profile_report.qdrep\n",
      "Exporting 440853 events: [================================================100%]\n",
      "Using profile_report.sqlite for SQL queries.\n",
      "Running [/usr/local/cuda-11.4/NsightSystems-cli-2021.2.4/target-linux-x64/reports/nvtxppsum.py profile_report.sqlite]... \n",
      "\n",
      "+---------+-----------------+-----------+--------------+------------+------------+-------------+------------------------+\n",
      "| Time(%) | Total Time (ns) | Instances |   Average    |  Minimum   |  Maximum   |   StdDev    |         Range          |\n",
      "+---------+-----------------+-----------+--------------+------------+------------+-------------+------------------------+\n",
      "|    34.4 |     37478830620 |         9 | 4164314513.3 | 4009035684 | 4449764942 | 167021085.2 | Batch                  |\n",
      "|    29.9 |     32541640013 |      6336 |    5135991.2 |      73214 |  278611198 |  16496450.2 | Preprocessing          |\n",
      "|    24.4 |     26513855612 |      5184 |    5114555.5 |    4481764 |  226140692 |   4109894.0 | ColorJitter            |\n",
      "|     5.3 |      5811509849 |        10 |  581150984.9 |  545857936 |  632503752 |  31406496.6 | Iteration              |\n",
      "|     2.4 |      2651022548 |      5184 |     511385.5 |      18305 |   73054811 |   1321842.3 | RandZoomd_1            |\n",
      "|     1.3 |      1395229370 |      5184 |     269141.5 |     222115 |     451398 |     35186.5 | ScaleIntensityRanged_1 |\n",
      "|     0.4 |       469210663 |      5760 |      81460.2 |      71147 |     417174 |     21686.3 | Postprocessing         |\n",
      "|     0.3 |       355362668 |      5184 |      68549.9 |      51581 |     138248 |     12232.6 | ToNumpyd_1             |\n",
      "|     0.3 |       304606782 |     10368 |      29379.5 |       9141 |     950694 |     43296.9 | ToTensord_1            |\n",
      "|     0.2 |       265824322 |      5184 |      51277.8 |      34891 |     100251 |      9679.1 | CastToTyped_1          |\n",
      "|     0.2 |       243262949 |      5760 |      42233.2 |      36621 |     177833 |     11170.9 | AsDiscreted_1          |\n",
      "|     0.2 |       230500688 |      5184 |      44463.9 |      12546 |     216107 |     36620.7 | RandRotate90d_1        |\n",
      "|     0.2 |       195372614 |      5184 |      37687.6 |       5675 |     162997 |     31817.5 | RandFlipd_1            |\n",
      "|     0.2 |       189212791 |      5760 |      32849.4 |      28531 |     206598 |      9289.4 | Activationsd_1         |\n",
      "|     0.1 |        89194559 |        10 |    8919455.9 |    8437244 |    9815047 |    505254.0 | ResNet18.__call__      |\n",
      "|     0.1 |        89028670 |        10 |    8902867.0 |    8425967 |    9796911 |    503973.9 | ResNet18.forward       |\n",
      "|     0.0 |         2585227 |        10 |     258522.7 |     244600 |     308403 |     24630.7 | Loss.__call__          |\n",
      "|     0.0 |         2513771 |        10 |     251377.1 |     237826 |     299605 |     24031.8 | Loss.forward           |\n",
      "+---------+-----------------+-----------+--------------+------------+------------+-------------+------------------------+\n",
      "\n",
      "Running [/usr/local/cuda-11.4/NsightSystems-cli-2021.2.4/target-linux-x64/reports/nvtxppsum.py profile_report.sqlite] to [profile_report_nvtxppsum.csv]... PROCESSED\n",
      "\n",
      "Running [/usr/local/cuda-11.4/NsightSystems-cli-2021.2.4/target-linux-x64/reports/nvtxpptrace.py profile_report.sqlite] to [profile_report_nvtxpptrace.csv]... PROCESSED\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Performance Analysis\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# Ordered list of NVTX range for all training transforms\n",
    "transforms = [\n",
    "    \"ToTensord_1\",\n",
    "    \"ColorJitter\",\n",
    "    \"ToNumpyd_1\",\n",
    "    \"RandFlipd_1\",\n",
    "    \"RandRotate90d_1\",\n",
    "    \"CastToTyped_1\",\n",
    "    \"RandZoomd_1\",\n",
    "    \"ScaleIntensityRanged_1\",\n",
    "    \"Activationsd_1\",\n",
    "    \"AsDiscreted_1\",\n",
    "]"
   ],
   "outputs": [],
   "metadata": {
    "lines_to_next_cell": 2
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Using Nsight Summary Report"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# Load NVTX Push/Pop Range Summary\n",
    "summary = pd.read_csv(\"profile_report_nvtxppsum.csv\")\n",
    "# display(summary)\n",
    "\n",
    "# Set the Range (which is the name of each range) as the index\n",
    "summary.set_index(\"Range\", inplace=True)\n",
    "\n",
    "# Get the entries for training transforms only (to avoid nested ranges)\n",
    "summary = summary.loc[transforms]\n",
    "\n",
    "# Normalize each transform range with total average time (percentage of transfom time)\n",
    "summary = summary[[\"Average\", \"Minimum\", \"Maximum\", \"StdDev\"]] / summary[\"Average\"].sum() * 100\n",
    "summary"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                          Average    Minimum      Maximum     StdDev\n",
       "Range                                                               \n",
       "ToTensord_1              0.473746   0.147399    15.330006   0.698166\n",
       "ColorJitter             82.472561  72.268754  3646.534292  66.272325\n",
       "ToNumpyd_1               1.105372   0.831747     2.229259   0.197252\n",
       "RandFlipd_1              0.607715   0.091510     2.628338   0.513059\n",
       "RandRotate90d_1          0.716983   0.202305     3.484740   0.590511\n",
       "CastToTyped_1            0.826858   0.562620     1.616554   0.156076\n",
       "RandZoomd_1              8.246127   0.295169  1178.013878  21.314798\n",
       "ScaleIntensityRanged_1   4.339925   3.581620     7.278824   0.567385\n",
       "Activationsd_1           0.529699   0.460064     3.331407   0.149792\n",
       "AsDiscreted_1            0.681013   0.590516     2.867569   0.180132"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average</th>\n",
       "      <th>Minimum</th>\n",
       "      <th>Maximum</th>\n",
       "      <th>StdDev</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Range</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ToTensord_1</th>\n",
       "      <td>0.473746</td>\n",
       "      <td>0.147399</td>\n",
       "      <td>15.330006</td>\n",
       "      <td>0.698166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ColorJitter</th>\n",
       "      <td>82.472561</td>\n",
       "      <td>72.268754</td>\n",
       "      <td>3646.534292</td>\n",
       "      <td>66.272325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ToNumpyd_1</th>\n",
       "      <td>1.105372</td>\n",
       "      <td>0.831747</td>\n",
       "      <td>2.229259</td>\n",
       "      <td>0.197252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandFlipd_1</th>\n",
       "      <td>0.607715</td>\n",
       "      <td>0.091510</td>\n",
       "      <td>2.628338</td>\n",
       "      <td>0.513059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandRotate90d_1</th>\n",
       "      <td>0.716983</td>\n",
       "      <td>0.202305</td>\n",
       "      <td>3.484740</td>\n",
       "      <td>0.590511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CastToTyped_1</th>\n",
       "      <td>0.826858</td>\n",
       "      <td>0.562620</td>\n",
       "      <td>1.616554</td>\n",
       "      <td>0.156076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandZoomd_1</th>\n",
       "      <td>8.246127</td>\n",
       "      <td>0.295169</td>\n",
       "      <td>1178.013878</td>\n",
       "      <td>21.314798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScaleIntensityRanged_1</th>\n",
       "      <td>4.339925</td>\n",
       "      <td>3.581620</td>\n",
       "      <td>7.278824</td>\n",
       "      <td>0.567385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Activationsd_1</th>\n",
       "      <td>0.529699</td>\n",
       "      <td>0.460064</td>\n",
       "      <td>3.331407</td>\n",
       "      <td>0.149792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AsDiscreted_1</th>\n",
       "      <td>0.681013</td>\n",
       "      <td>0.590516</td>\n",
       "      <td>2.867569</td>\n",
       "      <td>0.180132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "plt.style.use(\"fivethirtyeight\")\n",
    "plt.style.use(\"tableau-colorblind10\")\n",
    "axes = summary.plot.barh(\n",
    "    y=\"Average\",\n",
    "    xerr=\"StdDev\",\n",
    "    title=\"Average Time\",\n",
    "    xlabel=\"\",\n",
    "    fontsize=16,\n",
    "    figsize=(15, 9),\n",
    "    alpha=0.5,\n",
    "    legend=\"\",\n",
    ")\n",
    "axes.set_xlim([-1, 101])\n",
    "axes.set_xticks(range(0, 101, 10))\n",
    "axes.set_xlabel(\"% of preprocessing time\", fontsize=20)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 0, '% of preprocessing time')"
      ]
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ],
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NmqV8+fKpadOmGjdunEqWLHnPev7u1Vdf1axZs7R+/Xo1a9ZMc+bMUcuWLW2eWsqwYMECeXt76+zZs5o2bZpiYmJUo0YNhYaGSpLS0tIUGhqqU6dOafjw4SpXrpzy58+vNWvWKDo6OlO//n6vzs7ONvd6+vRpScpUi4ODg4oUKWJzLDExUZ9//rk+//zzLO/z/Pnzunbtmm7evCkPD49M32d1zOyGLf0hy+PhQX4GVwIAAAAAeNhkKxD6Oy8vL3Xr1k3Dhw/XgQMHVKNGDRUpUkQnT5684znXrl3Tf/7zHw0fPtxmM+Fff/31ntdzdXWVu7u7xo8fn+X3ublUyNXVVSVLlrzjBtb3+2SKnZ2devTooR49eigpKUmbNm3S22+/rZ49e2rjxo33XV/FihVVp04dffbZZ3riiSd08OBBTZo06Y5jM5acNWjQQIGBgYqIiFCrVq1UoEAB/fnnn9q5c6dmzpypkJAQ63lr166977okWZ8mSkxMtDmemppqs5G1dKvPderU0cCBA7Ocy8vLS46OjsqXL5/OnDmT6fszZ87Ix8fngeoEAAAAAMBs7hkInTp1SkWLFs10PGP9WsaTGY0bN9bq1avvOP769etKTU1Vvnz5bI4vXLjwnkUGBQXpk08+UfHixbN88iUnZDzdcvXqVRUqVMjm2itXrlSBAgVUvnz5HL2mxWJR27Zt9eOPP971jWm315aVjKVff/31l3x9fa17HN2Ns7OzRo8erc6dO2vWrFl67bXXdOXKFUmy+Tu6efOmvvjii/u4q//j7e0tb29vrVixwmYJ2+rVq22WhUm3+rxjxw5VqFBB+fPnv+Oc1atX18qVKxUeHm5dNvbjjz/qyJEjBEIAAAAAAGTTPQOhOnXqqFGjRmratKlKliyp5ORkxcfHa/bs2XrppZes/xIeHh6u+Ph4Pf/88xo8eLBKly6tkydPauPGjfrkk0/01FNPqVatWpo6dao8PT1VpEgRLViw4K5PFWXo37+/vvzyS7Vo0UL9+/eXr6+vrly5ooSEBG3dulVxcXH/uBF+freW0UydOlVNmzaVg4ODAgIC1LFjR8XGxqp169YaMGCA/P39dePGDf35559au3atYmNj72sPoNdff10FCxZU7dq15ebmpgMHDmjRokVq3LjxPWuLiYlRaGio8uXLp0qVKlmXo7Vq1Urh4eH6/vvvNXbs2GzX8sILL6h69eqaOnWqevfuLT8/P/n4+GjMmDFycHCQo6Ojpk+fnu35/s7e3l7Dhg3Ta6+9pn//+99q06aNDh06pEmTJqlw4cI2+wC99dZbCgoK0gsvvKDevXurRIkSSkpK0u+//65Dhw5p2rRpkm79nr300kvq3LmzevToobNnzyoyMtJmb6Ps2rlzp44cOaK0tDRJ0h9//KEVK1ZIkpo2bfrAezsBAAAAAPCwu2cgNHLkSK1fv17vvfeeEhMT5eDgoLJly2rUqFE2S79KliypDRs2aOzYsXr33Xd1+fJleXl52Wy4HBMTo8GDB2vIkCF64okn9NJLL6lLly42y5Oy8tRTT2n9+vWKiorS5MmTdfLkST311FMqV66cXnzxxX9w+/+nefPm6tWrl2bNmqUJEyYoPT1dSUlJypcvn5YtW6ZJkyZp7ty5Onz4sJ588kmVLl1azz//vDWUya5nnnlGsbGxWrRokS5evKiiRYuqY8eOmTZevp2/v7+GDx+uuXPnau7cuUpLS9Pu3butew7ly5dPL7zwgj7//HObt7plx9tvv622bdtq9uzZGjBggGJjYzV06FD17dtXLi4u6tKli3x8fPTaa6/d17wZunXrpkuXLmn69OlavHixnn76aX3yyScKDQ1V4cKFreN8fHy0efNmjR8/XmPGjNHZs2fl6uqqp59+2rrHkXTr7XKffvqpxo8fr65du6pMmTKKjIzUxx9/fN+1ffLJJzZh4vLly7V8+XJJsukvAAAAAACPG7ukpKT0vC4C/0xKSooCAgJUp04dffLJJ3ldzj3t3LlTjRs31scff6xOnTrldTk5bsjibQ/1W8Yet02lecOBsei3sei3sei3sei3sei3sei3sei38ei5sfL0LWN4OF28eFG///67lixZouPHj+tf//pXXpeUyaFDhxQTE6M6deqoUKFC+t///qcPPvhAJUuWVKtWrfK6PAAAAAAATIlA6BG2e/duvfjii9Y3sFWpUiWvS8okf/78+v333/X5558rKSlJFotFjRo10jvvvJNre/SkpqYqPf3OD77Z29vb7F8EAAAAAIDZEAg9wurXr6+kpKS8LuOuPD09tXTpUkOvWa1aNR09evSO3w8bNuyuezYBAAAAAPC4IxDCY+fzzz/XjRs37vh90aJFDawGAAAAAICHD4EQHjuVKlXK6xIAAAAAAHioEQgBOSwswI0d9wEAAAAADzV21gUAAAAAADAZAiEAAAAAAACTIRACAAAAAAAwGQIhAAAAAAAAkyEQAgAAAAAAMBkCIQAAAAAAAJMhEAIAAAAAADAZAiEAAAAAAACTIRACAAAAAAAwGQIhAAAAAAAAkyEQAgAAAAAAMBkCIQAAAAAAAJMhEAIAAAAAADAZAiEAAAAAAACTIRACAAAAAAAwGQIhAAAAAAAAkyEQAgAAAAAAMBkCIQAAAAAAAJMhEAIAAAAAADAZAiEAAAAAAACTIRACAAAAAAAwGQIhAAAAAAAAkyEQAgAAAAAAMBkCIQAAAAAAAJMhEAIAAAAAADAZx7wuAHjcfLLzrFyPpOV1GY+E8CC/vC4BAAAAAEyJJ4QAAAAAAABMhkAIAAAAAADAZAiEAAAAAAAATIZACAAAAAAAwGQIhAAAAAAAAEyGQAgAAAAAAMBkCIQAAAAAAABMhkAol8TGxspisVh/3N3dVa1aNY0ePVrXrl0zvB5/f3/169fP5vPt9WX1s2XLFsPrfFCRkZGyWCz3dc7atWvVq1cv1ahRQy4uLgoODs6d4gAAAAAAeMg45nUBj7u5c+eqWLFiunTpklavXq0PPvhAycnJev/99/O0rgULFujGjRuZjl+8eFE9evRQ4cKF5e/vnweVGWfNmjXau3evatasqevXr+d1OQAAAAAAGIZAKJf5+/urTJkykqTGjRvrwIEDio2NVVRUlOzt8+4BrapVq2Z5/OWXX9bVq1e1bNmy+37i5lHz0UcfWf8OmjdvnsfVAAAAAABgHAIhg1WtWlVff/21zp07J3d3d23atEkzZszQnj17dPHiRZUqVUqdO3dW//795eDgYD3P399fderUUbNmzRQVFaVjx46pfPnyioyMVJ06dWyuMWPGDE2fPl1nzpzR008/rffeey9btU2ZMkWrV69WZGSkatasafPdTz/9pNGjR+vHH39Uenq6atasqXfeeUc1atSwGbdo0SJ99NFH2r9/vwoUKKDnnntOo0ePVtGiRTPdS5MmTRQdHa1jx44pICBAU6dOVdGiRTVixAitWLFCjo6O6tixo9599105Ov7fr+ru3bs1fPhw/fzzz3J1dVWPHj2Unp6e7b+DDA8ayD3uQZmRovK6AOAhk5SUlNclAAAAwCQIhAx25MgRFS5cWK6urpKkQ4cOqUGDBgoLC5Ozs7N27dqlqKgonTt3TqNGjbI5d+vWrUpISNCIESPk7OyscePGKSQkRHv27LGGFPPmzVN4eLg6d+6stm3b6uDBg+rVq5cuXbp017q2bt2qd999V61atbLZa0iSfvnlFwUHB8vPz0/Tp0+XJE2ePFnBwcGKj4+3Li2bM2eOBg4cqLZt2+qdd97RqVOnNHr0aP3000/65ptvVLBgQZvr/fnnnxo1apRu3ryp8PBwde3aVaVKlVKZMmU0e/Zsfffdd4qOjlbp0qXVq1cvSdK5c+fUqlUreXp6asaMGXJyctKUKVN07NixB/47AQAAAADAbAiEcllqaqpSUlJ06dIlrVq1SitXrlRkZKT16Z+ePXtax6anp6tu3bq6ceOGpkyZooiICJunWJKTk/Xtt99awx9PT081btxY8fHx6tChg9LS0hQVFaWgoCBrcCNJbm5uNtf5uzNnzujVV19ViRIlNHXq1EzfT5gwQU5OTlqxYoX12o0bN1aVKlUUFRWlBQsWKDU1VePGjVO9evU0e/Zs67nlypVTixYttGDBAvXt29d6/NKlS1qyZImeeuopSdLp06c1fPhw1ahRQ2PHjrVeY/369Vq+fLk1EJo+fbquXLmiZcuWqXjx4tZxj/t+RwDMISEhIVvHkHvot7Hot7Hot7Hot7Hot/HoubHu1u9y5co90JwEQrmsVq1aNp979eqlsLAw6+dTp05p/Pjx2rBhg06dOqWUlBTrd4mJifL09LR+rl27ts1ypYoVK0qS9emY48eP6/jx4xo+fLjNNVu1amWz5Op2qampevXVV3XhwgUtWrRIhQsXzjRm69atat68uc21CxcurBYtWmjdunWSbv1yJiYmauTIkTbn1qlTRz4+Pvruu+9sAqHatWtbwyBJKl++vCQpKCjI5vzy5cvrp59+sn7evn27atasaQ2DJKlAgQJq3ry5Fi5cmOU9AsCj4u//ZZ6QkPDA/wWP+0e/jUW/jUW/jUW/jUW/jUfPjZVb/SYQymULFiyQt7e3zp49q2nTpikmJkY1atRQaGio0tLSFBoaqlOnTmn48OEqV66c8ufPrzVr1ig6OjrT6+ldXFxsPjs7O0uSddzp06clSR4eHjbjHB0drUvU/m7cuHHasmWLPvzwQ1WpUiXLMRcuXLAJpjJ4enpa97u4cOGC9VhW4zK+z/D3fXjy5ct3x+O3vwHs9OnTevrppzNd4+/3nJvutcfHkMXb5Fok637DVniQ3z+eg/8yMhb9BgAAAB4PBEK5rGLFita3jDVo0ECBgYGKiIhQq1atdOrUKe3cuVMzZ85USEiI9Zy1a9c+0LUywpgzZ87YHE9JSdH58+czjV+3bp0mTZqkjh07qnv37nec18XFxRo23e706dPWACcjrLrTuGrVqmX3Nu7K09Mz0/1Jme8ZAAAAAADcWd6999yEnJ2dNXr0aCUmJmrWrFm6cuWKpP97OkaSbt68qS+++OKB5vf29lbx4sW1fPlym+MrV660WYomSYcPH1bfvn3l5+enSZMm3XXewMBAxcfHKzk52XosOTlZ69atU7169STdWubg4eGhZcuW2Zz7ww8/6OjRo9Zx/1Tt2rX1448/2mwiffnyZevSNQAAAAAAcG88IWSwF154QdWrV9fUqVPVvXt3+fj4aMyYMXJwcJCjo6PNZtD3y97eXkOHDtVrr72m/v37q127djp48KAmT55sszfQjRs31L17d/31118aO3asfvvttyznK126tNzc3DRkyBB99dVXat26tV5//XXZ2dnpww8/1NWrVzV06FBJkoODg9566y0NHDhQYWFh6tixo06cOKGxY8eqbNmyevnllx/4vm7Xv39/xcTEqG3btho+fLj1LWP58+e/77mOHDminTt3SpLOnz8ve3t7rVixQpIUEBCgEiVK5EjNAAAAAAA8bAiE8sDbb7+ttm3basGCBYqNjdXQoUPVt29fubi4qEuXLvLx8dFrr732QHN369ZNly9f1rRp07R06VI9/fTTiomJsdnI+uTJk9q1a5ck6V//+tcd55o2bZq6dOmiypUra/Xq1RozZoz69++v9PR01axZU2vWrLF5u9crr7yi/Pnz66OPPlLnzp1VoEABNW3aVKNHj1aBAgUe6H7+rkiRIlqxYoXCw8PVr18/ubq6qkePHkpJSdGECRPua64tW7ZowIABNscyls5l3DsAAAAAAI8ju6SkpPS8LgJ4nLCpdPaxqfSjh34bi34bi34bi34bi34bi34bi34bj54bK7f6zR5CAAAAAAAAJsOSMTx20tLSlJaWdsfv7ezs5ODgYGBFAAAAAAA8XHhCCI+dAQMGyM3N7Y4/rVq1yusSAQAAAADIUzwhhMfO8OHDbTbR/ruCBQsaWA0AAAAAAA8fAiE8dkqWLKmSJUvmdRkAAAAAADy0CISAHBYW4MaO+wAAAACAhxp7CAEAAAAAAJgMgRAAAAAAAIDJEAgBAAAAAACYDIEQAAAAAACAyRAIAQAAAAAAmAyBEAAAAAAAgMkQCAEAAAAAAJgMgRAAAAAAAIDJEAgBAAAAAACYDIEQAAAAAACAyRAIAQAAAAAAmAyBEAAAAAAAgMkQCAEAAAAAAJgMgRAAAAAAAIDJEAgBAAAAAACYDIEQAAAAAACAyRAIAQAAAAAAmAyBEAAAAAAAgMkQCAEAAAAAAJgMgRAAAAAAAIDJEAgBAAAAAACYDIEQAAAAAACAyRAIAQAAAAAAmAyBEAAAAAAAgMkQCAEAAAAAAJiMY14XADxuPtl5Vq5H0rL8LjzIz+BqAAAAAADIjCeEAAAAAAAATIZACAAAAAAAwGQIhAAAAAAAAEyGQAgAAAAAAMBkCIQAAAAAAABMhkAIAAAAAADAZAiEAAAAAAAATIZACAAAAAAAwGQIhHLJ9u3b9corr6hChQpyd3dX6dKl1aZNGy1cuFCpqak5dp09e/YoMjJSFy5csB6LjIyUxWK5509sbOwd5+3Xr1+25tiyZUuO3cs/ERsbK4vFosOHD2f7nG3btqlfv36qU6eOihQpIn9//1ysEAAAAACAh4djXhfwOJo+fbpGjBihBg0aaNSoUfLx8VFSUpI2b96swYMH66mnnlJwcHCOXGvv3r2KiopSSEiIXFxcJEndunXTc889Zx3z1VdfKTo6WnPnzlWxYsWsx0uXLn3HeYcOHaqePXtaP8+bN0/z58/XunXr5ODgYD3u5+eXI/eRF7755htt27ZNAQEBsrOzU3Jycl6XBAAAAACAIQiEcth3332nESNGqHfv3powYYLNd8HBwRowYIAuX76cqzV4e3vL29vb+vl///ufJMnf319lypTJ1hylS5e2CYw2bNggSapZs6YcHR+PX5uhQ4dq+PDhkqSwsDBt27YtjysCAAAAAMAYLBnLYR9++KFcXFw0evToLL8vXbq0KleurLNnz2rgwIGqUaOGvLy8VKlSJfXq1UsnTpywGb9//3516dJFvr6+8vT0VOXKldW9e3elpKQoNjZWAwYMkCRVr17duowrO8umbt68qbFjx8rf31/u7u7y9/fX2LFjdfPmzWzd55AhQ1SuXLlM45OTk1W8eHGNGjVKkrRlyxZZLBatWLFC/fr1U8mSJeXj46PevXvr/PnzNuempKTogw8+UK1ateTh4aEKFSpoxIgRunbtms24Q4cOqWPHjvLy8lLZsmU1bNgw3bhxI1t1387e3thf/6h2z1j/jgAAAAAAyEuPx6MeD4nU1FRt2bJFwcHBeuKJJ+469sKFC3J2dlZERITc3Nx06tQpTZ06Vc2aNdOOHTus53fs2FEWi0UTJ05UkSJFdPLkScXHxystLU3NmjXTm2++mWk5WNGiRe9Za79+/fTll19q0KBBqlOnjn744QdNnDhRhw4dUkxMzD3Pf/XVV/Xpp59q9erVeumll6zHlyxZosuXL+uVV16xGf/WW2+pYcOGmjVrlg4cOKAxY8bo5MmTWr16tXVMWFiY1q1bp9dff13PPPOM/vjjD40bN05HjhzR/PnzJUk3btxQmzZtdO3aNb3//vtyd3fXZ599ZjMPAAAAAAC4OwKhHHTu3DldvXpVPj4+9xxbrlw5RUVFWT+npqbqmWeeUeXKlRUfH68XX3xR586d08GDB7Vw4UK98MIL1rEdOnSQJLm5uVmXdd3PcrDffvtNS5Ys0bBhwxQeHi5JatKkiRwdHTVu3DgNHDhQlStXvuscFSpUUGBgoD777DObQOizzz5TkyZNVKpUqUzjp0+fLkl67rnn5OLiorCwMH3zzTdq2LChtm7dqmXLlmnGjBkKDQ2VJDVq1Mg6bs+ePapSpYri4uJ06NAhxcfHq1atWpKkpk2bqm7dutm695yQkJBwzzHnz52/6/fZmQPZRz+NRb+NRb+NRb+NRb+NRb+NRb+NRb+NR8+Ndbd+lytX7oHmJBDKQ7NmzdLs2bN16NAhm32F9u/fL0lydXVVqVKl9O677yoxMVH16tVT2bJl//F1t27dKkkKCQmxOd6xY0eNGzdO33333T0DIUnq1auXevbsqQMHDqhs2bL6+eeftWfPHuvTPLdr06ZNps99+/bV9u3b1bBhQ23cuFFOTk5q3bq1UlJSrOOaNGlirblKlSravn27ihcvbg2DpFtLv9q0aaPx48dnuwf/xD3/Ydt5Vq5FXP/ZHMi2hIQE+mkg+m0s+m0s+m0s+m0s+m0s+m0s+m08em6s3Oo3ewjlIFdXV+XPn19Hjx6959iZM2dq8ODBatSokebPn69NmzZZN27O2DPHzs5Oy5cvV7Vq1fTuu++qRo0aqlq1qmbNmvWP6sx4Rb2np6fN8YzPt7/C/m5atmwpT09PffbZZ5Kk2bNny8vLSy1atMg01sPDw+azk5OTLBaLTp48KUlKTEzUjRs3VKxYMbm5uVl/fH19Jcm639Dp06fl7u5+z/kfRsOW/qCkpCQlJSXldSkAAAAAAJPjCaEc5OjoqHr16mnz5s26fv26nJ2d7zh22bJlatiwocaNG2c9dujQoUzjSpUqpZkzZyo9PV179+7Vp59+qsGDB6tEiRJq2rTpA9WZ8Xr6M2fO2LxJ7PTp0zbf30u+fPnUtWtXzZo1S6+//rqWLVumAQMGZPkWsjNnzth8vnHjhpKSkuTl5SXpVpj2xBNPaO3atVleK2NfJE9PT+3bt++e8wMAAAAAgDvjCaEcNnDgQJ0/f14RERFZfn/o0CH98ssvunLlivLly2fzXWxs7B3ntbOzU5UqVawB0u+//y5J1tDp6tWr2a4xY7+dpUuX2hz/4osvJEn16tXL9lw9evTQX3/9pe7du+v69evq3r17luOWL1+e6XNaWppq164tSQoKCtK1a9d08eJFBQQEZPrJCI5q166tY8eOaceOHda50tLSMs0PAAAAAADujCeEclhgYKDGjRunESNG6I8//lDnzp1VvHhxJSUl6ZtvvtH8+fP16aef6rnnntPkyZM1ceJE1ahRQ//973+1YsUKm7l++eUXDR8+XG3btlWZMmWUmpqqhQsXytHRUQ0aNJAk+fn5SZJiYmIUGhqqfPnyqVKlSnJycrpjjRUrVlT79u01fvx4paSk6JlnntH27dv1/vvvq3379qpUqVK277dYsWJq0aKFVq9erebNm6t48eJZjtu3b5/69++vdu3aaf/+/Ro7dqzq1aunhg0bSpLq16+v9u3bq1u3bhowYIBq1Kghe3t7HTlyROvXr9e7774rX19fhYaGatKkSeratatGjhwpd3d3zZ49W8nJydmuOcPZs2f13XffSZKOHj2qq1evWv8O/Pz8VKFChfueEwAAAACARwGBUC7o37+/atSooenTp2vkyJE6d+6cChYsqICAAE2aNEktWrRQkyZN9Ndff2n69Om6fv26AgMDtXTpUlWrVs06j6enp4oXL65p06bpxIkTcnZ2VsWKFbVo0SLrOH9/fw0fPlxz587V3LlzlZaWpt27d6tkyZJ3rXH69OkqVaqUFixYoOjoaBUtWlQDBw7UsGHD7vt+27Rpo9WrV6tHjx53HBMZGam1a9eqR48eSktLU/PmzW3esiZJn3zyiWbOnKkFCxZo4sSJcnZ2lo+Pj4KCgqz7Bjk5OWn58uUaMmSI3nzzTT355JNq3769mjVrpjfeeOO+6v79998zPdGU8fn2N7ABAAAAAPC4sUtKSkrP6yLwaOvdu7e+//577d69W/b2tqsQt2zZohdffFHLly9Xo0aN8qZAgw1ZvO2ObxkLD/IzuJrHH284MBb9Nhb9Nhb9Nhb9Nhb9Nhb9Nhb9Nh49N1Zu9ZsnhPDAduzYob1792rZsmUaN25cpjAIAAAAAAA8nAiE8MCaNm2qggULKjQ0VL169crrcqzS0tKUlpZ2x+/t7Ozk4OBgYEUAAAAAADxcCITwwJKSku45pn79+tkal5OioqIy7U90Ox8fH+3du9fAigAAAAAAeLgQCOGx88orr6h58+Z3/P5ub2ADAAAAAMAMCITw2PHy8pKXl1delwEAAAAAwEOLQAjIYWEBbuy4DwAAAAB4qPFaKAAAAAAAAJMhEAIAAAAAADAZAiEAAAAAAACTIRACAAAAAAAwGQIhAAAAAAAAkyEQAgAAAAAAMBkCIQAAAAAAAJMhEAIAAAAAADAZAiEAAAAAAACTIRACAAAAAAAwGQIhAAAAAAAAkyEQAgAAAAAAMBkCIQAAAAAAAJMhEAIAAAAAADAZAiEAAAAAAACTIRACAAAAAAAwGQIhAAAAAAAAkyEQAgAAAAAAMBkCIQAAAAAAAJMhEAIAAAAAADAZAiEAAAAAAACTIRACAAAAAAAwGQIhAAAAAAAAkyEQAgAAAAAAMBkCIQAAAAAAAJNxzOsCgMfNJzvPyvVIWqbj4UF+eVANAAAAAACZ8YQQAAAAAACAyRAIAQAAAAAAmAyBEAAAAAAAgMkQCAEAAAAAAJgMgRAAAAAAAIDJEAgBAAAAAACYDIEQAAAAAACAyZgqEIqNjZXFYrH+uLu7q1q1aho9erSuXbtmeD3+/v7q16+f9fOWLVts6itSpIgqV66swYMHKykp6b7n37NnjyIjI3XhwoUHqu/w4cOKjIzUoUOHHuh8Sbpy5YrCw8P19NNPy8PDQ3Xr1tXixYuzHLt69WrVr19fnp6eqly5st5//32lpqZm6zoWi0WRkZHZris5OVlvv/22goOD5ePjI4vFoi1btmT7fAAAAAAAHmWmCoQyzJ07V/Hx8Vq8eLGCgoL0wQcfaOTIkXldllVUVJTi4+O1bNkyhYSEaM6cOerTp899z7N3715FRUU9cCB05MgRRUVF/aNAqGvXroqNjdXAgQP1+eef65lnnlFYWJgWLVpkM27jxo3q1q2bqlevri+++EJ9+/ZVdHS0Ro8e/cDXvpvz589rwYIFcnR0VKNGjXLlGgAAAAAAPKwc87qAvODv768yZcpIkho3bqwDBw4oNjZWUVFRsrfP+4zMz89PtWrVkiQ1bNhQiYmJmjdvnk6fPi1PT888ri77tm3bpo0bN2ratGnq0qWLJKlJkyY6ceKE3nnnHbVv314ODg6SpFGjRunZZ5/Vhx9+KElq0KCBLl++rOjoaPXv3z/H77tEiRLWoOvrr7/WqlWrcnR+AAAAAAAeZnmffjwEqlatqitXrujcuXOSpE2bNqlDhw7y8/OTl5eX6tSpoylTpmRavuTv76+wsDAtXbpUtWvXVrFixdSoUSNt27Yt0zVmzJghf39/eXp6qlGjRtq6det91SdJx44dsx67ePGihgwZogoVKsjDw0M1a9bUtGnTlJ6eLunW8rgBAwZIkqpXr25dhnb48GFJ0ieffKKmTZuqVKlSKlGihJ577jl99dVX1vm3bNmiF198UZLUpk0b6/m3L6uaM2eOAgMD5enpqTJlyuhf//qXzdNIP/74oySpadOmNvcTFBSkU6dOaceOHdb72rt3r0JCQmzGhYSE6ObNm4qPj7ceS01N1dixY61/N8HBwfr999+z3csMdnZ2933O/Yhq90ymn9uXA97+AwAAAACA0Uz5hNDfHTlyRIULF5arq6sk6dChQ2rQoIHCwsLk7OysXbt2KSoqSufOndOoUaNszt26dasSEhI0YsQIOTs7a9y4cQoJCdGePXus/7I/b948hYeHq3Pnzmrbtq0OHjyoXr166dKlS9muz8HBQSVKlJAkpaWlKSQkRLt371Z4eLgqVaqkr776SiNGjNC5c+cUERGhZs2a6c0331R0dLTmzp2rYsWKSZKKFi1qnbNr164qWbKkUlJStG7dOoWEhGjJkiV67rnnVLVqVUVHR+vNN99UVFSUqlevLunW00vSrSd6pk6dqj59+mjMmDE6ceKExo0bp99//13r16+Xg4OD9WkrJycnm/txdnaWJP3+++969tlntW/fPknS008/bTOuVKlSevLJJ/XHH39Yj0VGRmrixIkaMGCAmjRpop07dyo0NDRbfQQAAAAAALeYMhBKTU1VSkqKLl26pFWrVmnlypWKjIy0Ll/q2bOndWx6errq1q2rGzduaMqUKYqIiLBZVpacnKxvv/3WGv54enqqcePGio+PV4cOHZSWlqaoqCgFBQVp+vTp1vPc3NxsrnO7tLQ0paSk6OrVq/rmm280e/Zs9evXT+7u7pKk9evXa9u2bZmWYl25ckVTp07VgAED5ObmptKlS0uyXSKXYezYsTbXa9iwofbv369Zs2bpueeeU+HCha3hz+1L2KRbm01/9NFHGjZsmIYNG2Y97uvrq+bNm2vt2rVq2bKlypUrJ0nasWOHzVNC27dvlyTr00QZ/zerp2UsFov1+6SkJM2YMUOvvPKKtf4mTZrIwcEhU1CXmxISEh7q+cyIHhqLfhuLfhuLfhuLfhuLfhuLfhuLfhuPnhvrbv3O+Hfv+2XKQOj2cEOSevXqpbCwMOvnU6dOafz48dqwYYNOnTqllJQU63eJiYk2+9nUrl3bJsioWLGipP9b3nX8+HEdP35cw4cPt7lmq1at5OiYdfvbtm1r87lZs2YaM2aM9fPWrVtlb2+vDh062Izr2LGj5s+fr+3bt6tFixZ3vH9J2rVrlyIjI/Xzzz/r7Nmz1qVm2flF+vrrr5WWlqaOHTva9KZmzZoqVKiQtm7dqpYtW6pJkyby8/PTsGHD9NRTT6l8+fJauXKlli5dKkn3vV/Tr7/+qsuXL6tNmzY2x9u2bWtoIHTPHu08m7Pz4a4SEhLooYHot7Hot7Hot7Hot7Hot7Hot7Hot/HoubFyq9+mDIQWLFggb29vnT17VtOmTVNMTIxq1Kih0NBQpaWlKTQ0VKdOndLw4cNVrlw55c+fX2vWrFF0dHSm19O7uLjYfM5YDpUx7vTp05IkDw8Pm3GOjo7WJWp/Fx0drRo1aujixYuaO3euli1bpgkTJlifxrlw4YJcXFwyLcXKCKru9VaxY8eOqVWrVqpQoYImTJig4sWLy9HRUePGjbNZnnUniYmJkqSAgIAsvz9//rz1HufOnatevXrp+eefl3SrDxEREXrrrbes9WYEaklJSZnmSkpKsvb4Tr38++eHwbClP2Q6Fh7klweVAAAAAACQmSkDoYoVK1qXUDVo0ECBgYGKiIhQq1atdOrUKe3cuVMzZ8602eR47dq1D3StjNDjzJkzNsdTUlKswcnf+fr6WsOWBg0a6MyZM5o0aZK6dOmi4sWLy8XFRRcuXNCNGzdsQqGMwOTvIdXfbdy4URcvXtRnn30mb29v6/ErV65k654ygqwvv/wyy2Vet1+/QoUK+vbbb3X48GFduXJFvr6+1jd6Pfvss9YxkrRv3z7Vrl3bem7GORlL127v5e37Df29twAAAAAA4O5M/5YxZ2dnjR49WomJiZo1a5Y1FMmXL591zM2bN/XFF1880Pze3t4qXry4li9fbnN85cqVNsut7sTOzk7vvfeerl+/rsmTJ0uSAgMDlZaWlmnOL774Qk5OTtZQJeNppatXr9qMy+oe9+/frx9+sH2q5U7nN27cWPb29jp69KgCAgIy/ZQqVSrTfZQsWdIa4nz66adq0qSJdY8jHx8fVa5cWYsXL7Y5Z/HixcqXL591/6FKlSqpQIECme572bJlma4HAAAAAADuzJRPCP3dCy+8oOrVq2vq1Knq3r27fHx8NGbMGDk4OMjR0dFmM+j7ZW9vr6FDh+q1115T//791a5dOx08eFCTJ09W4cKFszVHlSpV1KpVK82fP1+DBw9W06ZNVadOHQ0aNEhnz57V008/rfXr12vevHkaNGiQihQpIun/3ggWExOj0NBQ5cuXT5UqVVKjRo3k6Oiovn376l//+pdOnTqlyMhIFS9eXGlpadbr+vr6ytHRUQsWLJCLi4ucnZ3l6+ur0qVLa+DAgRo6dKj279+vwMBAPfHEEzp27Ji+/vprde3aVQ0aNJAkffDBB/Lx8VHRokV17NgxxcTE6NixYzavuJekiIgIhYSEaODAgWrXrp327Nmj6Oho9e3b12ZpWb9+/TRx4kQVLFhQTZo00c8//6z58+c/0N9NfHy8rly5ol9//VWS9N133+n8+fN68sknbTbBBgAAAADgcUMg9P+9/fbbatu2rRYsWKDY2FgNHTpUffv2lYuLi7p06SIfHx+99tprDzR3t27ddPnyZU2bNk1Lly7V008/rZiYGJuNrO/lrbfe0qpVqzR58mRFRUVp0aJFGjNmjD788EOdP39eJUqU0Lhx49S/f3/rOf7+/ho+fLjmzp2ruXPnKi0tTbt379bTTz+tTz/9VO+9955CQ0NVunRpjRo1Shs2bNC3335rPd/V1VXvv/++Jk+erODgYKWmpmrVqlWqX7++IiIiVL58ecXExCgmJkZ2dnby9vZWw4YNVbZsWescV65c0ZgxY3Tq1Ck99dRTCgoK0ty5c1W8eHGb+3v++ec1d+5cRUVFaeHChfLw8NCgQYP05ptv2owLDw+XJM2bN0+ffvqpatSooc8//9y6/Ox+DBo0SEePHrV+Hj9+vKRbTyzt3bv3vucDAAAAAOBRYZeUlJSe10UAj5Mhi7fJtUjmDcPZVDp38IYDY9FvY9FvY9FvY9FvY9FvY9FvY9Fv49FzY+VWv02/hxAAAAAAAIDZsGQMj517bdbt4OAgOzs7g6oBAAAAAODhQyCEx8rhw4dVtWrVu47J2AcJAAAAAACzIhDCY8XLy0ubN2++6xhfX1+DqgEAAAAA4OFEIITHipOTkwICAvK6DAAAAAAAHmoEQkAOCwtwY8d9AAAAAMBDjbeMAQAAAAAAmAyBEAAAAAAAgMkQCAEAAAAAAJgMgRAAAAAAAIDJEAgBAAAAAACYDIEQAAAAAACAyRAIAQAAAAAAmAyBEAAAAAAAgMkQCAEAAAAAAJgMgRAAAAAAAIDJEAgBAAAAAACYDIEQAAAAAACAyRAIAQAAAAAAmAyBEAA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     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "axes = summary.plot.barh(\n",
    "    y=[\"Average\", \"Minimum\", \"Maximum\"],\n",
    "    xlabel=\"\",\n",
    "    fontsize=16,\n",
    "    figsize=(15, 15),\n",
    "    alpha=0.5,\n",
    "    subplots=True,\n",
    "    legend=\"\",\n",
    ")\n",
    "axes[-1].set_xlim([0, 101])\n",
    "axes[-1].set_xticks(range(0, 101, 10))\n",
    "axes[-1].set_xlabel(\"% of preprocessing time\", fontsize=20)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 0, '% of preprocessing time')"
      ]
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ],
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg height=\"952.8597pt\" version=\"1.1\" viewBox=\"0 0 1155.19953 952.8597\" width=\"1155.19953pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-08-24T14:15:56.931499</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.4.2, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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"
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 "metadata": {
  "interpreter": {
   "hash": "98b0a9b7b4eaaa670588a142fd0a9b87eaafe866f1db4228be72b4211d12040f"
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   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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 "nbformat_minor": 2
}
